Senior Data Scientist / ML Engineer (Forecasting) | NDA

Job description

**GT was founded in 2019 by a former Apple, Nest, and Google executive.** GT’s mission is to connect the world’s best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands. Our clients operate in industries like **healthcare, life sciences, fintech, retail, e-commerce, finance and many more** - giving our team exposure to real-world, high-impact projects. ## **About the Role** We’re looking for a **Senior Data Scientist / ML Engineer** to join a UK-based client in the healthcare and pharmacy domain. The role combines forecasting and machine learning with end-to-end ownership of solution delivery, from project discovery and stakeholder collaboration through model development, deployment, and productionisation. **Location**: Nottingham, UK **Office attendance**: up to 3 days per week in the Nottingham office. **Project Details**: The project focuses on developing a forecasting solution for a large healthcare network. It uses historical clinic and marketing data to predict clinic usage and staffing needs, helping optimize scheduling and resource allocation. The goal is to build a scalable, data-driven platform that improves operational efficiency. ## **Responsibilities:** - Design, train, and deploy ML models for time-series forecasting and related data tasks - Build and maintain data pipelines using cloud-native tools (AWS, GCP, or Azure) - Develop and optimize forecasting models (Prophet, ARIMA, LSTM, TimeGPT) - Collaborate with data, product, and cloud engineers to deliver reliable, scalable solutions - Participate in different stages of the project lifecycle - from discovery and PoC to production deployment, presenting your work to stakeholders - Work closely with business stakeholders and SMEs to gather requirements, shape solutions, and drive project discovery - Communicate modelling approaches, assumptions, and results to both technical and non-technical audiences ## **Essential knowledge, skills & experience (must-have):** - 4+ years of commercial experience in Data Science / Machine Learning - Hands-on experience with: - Databricks - Notebooks - PySpark - Workflows - Deployment through Asset Bundles - Proven experience building, deploying, and maintaining production ML solutions - Broad experience across multiple ML domains, including: - **Forecasting / Time-Series Modelling** - Regression - Classification - Gradient Boosting models (e.g. XGBoost, LightGBM) - Strong **Python** skills (Pandas, NumPy, scikit-learn, PyTorch) - Experience with model evaluation, performance monitoring, and accuracy metrics - Version control (Git) - Experience working with cloud environments (Azure preferred, AWS/GCP also considered) - SQL - **Fluent English** ## **Nice-to-have:** - Retail or similar consumer-facing industry experience - Azure DevOps: - Repos - Boards - Pipelines - Experience with Databricks model training and inference workflows - Databricks Apps and Lakebase - Experience with RAG pipelines - Experience with vector databases (Weaviate, Milvus) - Familiarity with LLM evaluation frameworks (e.g. DeepEval) ## **Soft Skills** - Strong sense of ownership and accountability - Strong stakeholder management skills - Proactive attitude and ability to work independently - Clear and confident communication with both tech and non-tech stakeholders - Comfortable working in ambiguity and helping define requirements - Strategic thinking and focus on business impact - Team player ## **Interview Steps** 1. GT interview with Recruiter 2. Technical interview 3. Cultural fit interview 4. Final interview 5. Reference check 6. Security check